Detecting Cardiac Abnormalities with Multi-Lead ECG Signals: A Modular Network Approach

Clark, Ryan; Heydarian, Mohammadreza; Siddiqui, Kashif; Rashidiani, Sajjad; Khan, Md Asif; Doyle, Thomas E.

doi:10.23919/cinc53138.2021.9662677

Cited by 1 publication

(2 citation statements)

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“…Many algorithms segmented the signals into windows during preprocessing. For example, Biomedic2ai segmented the signals into 5-second windows with a stride of one second for a 4-second overlap for adjacent signals (Clark et al 2021). snu_adsl selected a random window with a width of 13.3 seconds and zero-padded ECG signals shorter than 13.3 seconds at the end of the signal (Suh et al 2021).…”

Section: Rank Team [Reference]mentioning

confidence: 99%

“…Although 90% of the 39 official entries used DL models, about 40% of the algorithms combined handcrafted features with their DL models (Clark et al 2021, Alkhodari et al 2021. Team PhysioNauts was among the unofficial teams that used a ResNet model with a squeeze and excitation module with handcrafted and DL features and used a grid search and the Nelder-Mead method to optimize the Challenge evaluation metric (Garcia-Isla et al 2021).…”

Section: Rank Teammentioning

confidence: 99%

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Issues in the automated classification of multilead ecgs using heterogeneous labels and populations

et al. 2022

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Objective: The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge. In addition, we explore the potential for performance boosting through a meta-learning approach. Approach: We sourced 131,149 twelve-lead ECG recordings from ten international sources. We posted 88,253 annotated recordings as public training data and withheld the remaining recordings as hidden validation and test data. We challenged teams to submit containerized, open-source algorithms for diagnosing cardiac abnormalities using various ECG lead combinations, including the code for training their algorithms. We designed and scored algorithms using an evaluation metric that captures the risks of different misdiagnoses for 30 conditions. After the Challenge, we implemented a semi-consensus voting model on all working algorithms. Main results: A total of 68 teams submitted 1,056 algorithms during the Challenge, providing a variety of automated approaches from both academia and industry. The performance differences across the different lead combinations were smaller than the performance differences across the different test databases, showing that generalizability posed a larger challenge to the algorithms than the choice of ECG leads. A voting model improved performance by 3.5%. Significance: The use of different ECG lead combinations allowed us to assess the diagnostic potential of reduced-lead ECG recordings, and the use of different data sources allowed us to assess the generalizability of algorithms to diverse institutions and populations. The submission of working, open-source code for both training and testing and the use of a novel evaluation metric improved the reproducibility, generalizability, and applicability of the research conducted during the Challenge.

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Section: Rank Team [Reference]mentioning

confidence: 99%

Section: Rank Teammentioning

confidence: 99%